that sounds very good. In general, the "model" refers to thecollection of all weights and bias matrices of a given architecture.Similar to a classic regression model, we can view the weights as the"slope", i.e., multiplicative terms, while the biases are the"intercept", i.e., additive terms that shift the layer output. Bothare subject to training and thus part of the model.

This implies that the number of matrices in the model depends on thearchitecture. Hence, we have two choices here: (a) allow for avariable number of inputs and outputs, or (b) create a struct-likedata type that allows passing the collection of matrices via a singlehandle. We've discussed the second option in other contexts as wellbecause this would also be useful for reducing the number ofparameters passed through function calls. I'm happy to help outintegrating these struct-like data types if needed.

Great to see that you're in the process of updating the related JIRAs.Let us know whenever you think you're ready with an initial draft -then I'd make a detailed pass over it.

Furthermore, I would recommend to experiment with running theseexisting mnist lenet examples (which is one of our baselines movingforward):* Download the "infinite MNIST" data generator(http://leon.bottou.org/projects/infimnist), and generate a moderatelysized dataset (e.g., 256K instances).* Convert the input into SystemML's binary block format. The generatorproduces the data in libsvm format and we provide a data converter(see RDDConverterUtils.libsvmToBinaryBlock) to convert this into ourinternal binary representation.* Run the basic mnist lenet example for a few epochs.* Install the native BLAS libraries mkl or openblas and try using itfor the above example to ensure its setup and configured correctly.Regards,Matthias

On Sun, May 6, 2018 at 3:24 AM, Guobao Li <[EMAIL PROTECTED]> wrote:

> Hi Matthias,>> I'm currently reading the dml script MNIST LeNet example and got some> questions. I hope that you could help me out of them.>> 1) Is it possible to define a matrix containing the variables? Because I'm> wondering how to represent the model as a parameter for the "paramserv"> function.> 2) What is the role of bias? Why we need it?>> Additionally, I have added some updates in JIRA for SYSTEMML-2083 and hope> to get some feedback. Thanks!>> Regards,> Guobao>

that sounds very good. In general, the "model" refers to thecollection of all weights and bias matrices of a given architecture.Similar to a classic regression model, we can view the weights as the"slope", i.e., multiplicative terms, while the biases are the"intercept", i.e., additive terms that shift the layer output. Bothare subject to training and thus part of the model.

This implies that the number of matrices in the model depends on thearchitecture. Hence, we have two choices here: (a) allow for avariable number of inputs and outputs, or (b) create a struct-likedata type that allows passing the collection of matrices via a singlehandle. We've discussed the second option in other contexts as wellbecause this would also be useful for reducing the number ofparameters passed through function calls. I'm happy to help outintegrating these struct-like data types if needed.

Great to see that you're in the process of updating the related JIRAs.Let us know whenever you think you're ready with an initial draft -then I'd make a detailed pass over it.

Furthermore, I would recommend to experiment with running theseexisting mnist lenet examples (which is one of our baselines movingforward):* Download the "infinite MNIST" data generator(http://leon.bottou.org/projects/infimnist), and generate a moderatelysized dataset (e.g., 256K instances).* Convert the input into SystemML's binary block format. The generatorproduces the data in libsvm format and we provide a data converter(see RDDConverterUtils.libsvmToBinaryBlock) to convert this into ourinternal binary representation.* Run the basic mnist lenet example for a few epochs.* Install the native BLAS libraries mkl or openblas and try using itfor the above example to ensure its setup and configured correctly.Regards,Matthias

On Sun, May 6, 2018 at 3:24 AM, Guobao Li <[EMAIL PROTECTED]> wrote:

> Hi Matthias,>> I'm currently reading the dml script MNIST LeNet example and got some> questions. I hope that you could help me out of them.>> 1) Is it possible to define a matrix containing the variables? Because I'm> wondering how to represent the model as a parameter for the "paramserv"> function.> 2) What is the role of bias? Why we need it?>> Additionally, I have added some updates in JIRA for SYSTEMML-2083 and hope> to get some feedback. Thanks!>> Regards,> Guobao>

and access entries via l1[7] or l2['g'] accordingly. We're stillworking on additional features to make the integration with IPA,functions, and size/type propagation smoother, but the basicfunctionality is already available.

> Hi Guobao,>> that sounds very good. In general, the "model" refers to the> collection of all weights and bias matrices of a given architecture.> Similar to a classic regression model, we can view the weights as the> "slope", i.e., multiplicative terms, while the biases are the> "intercept", i.e., additive terms that shift the layer output. Both> are subject to training and thus part of the model.>> This implies that the number of matrices in the model depends on the> architecture. Hence, we have two choices here: (a) allow for a> variable number of inputs and outputs, or (b) create a struct-like> data type that allows passing the collection of matrices via a single> handle. We've discussed the second option in other contexts as well> because this would also be useful for reducing the number of> parameters passed through function calls. I'm happy to help out> integrating these struct-like data types if needed.>> Great to see that you're in the process of updating the related JIRAs.> Let us know whenever you think you're ready with an initial draft -> then I'd make a detailed pass over it.>> Furthermore, I would recommend to experiment with running these> existing mnist lenet examples (which is one of our baselines moving> forward):> * Download the "infinite MNIST" data generator> (http://leon.bottou.org/projects/infimnist), and generate a moderately> sized dataset (e.g., 256K instances).> * Convert the input into SystemML's binary block format. The generator> produces the data in libsvm format and we provide a data converter> (see RDDConverterUtils.libsvmToBinaryBlock) to convert this into our> internal binary representation.> * Run the basic mnist lenet example for a few epochs.> * Install the native BLAS libraries mkl or openblas and try using it> for the above example to ensure its setup and configured correctly.>>> Regards,> Matthias>> On Sun, May 6, 2018 at 3:24 AM, Guobao Li <[EMAIL PROTECTED]> wrote:>> Hi Matthias,>>>> I'm currently reading the dml script MNIST LeNet example and got some>> questions. I hope that you could help me out of them.>>>> 1) Is it possible to define a matrix containing the variables? Because I'm>> wondering how to represent the model as a parameter for the "paramserv">> function.>> 2) What is the role of bias? Why we need it?>>>> Additionally, I have added some updates in JIRA for SYSTEMML-2083 and hope>> to get some feedback. Thanks!>>>> Regards,>> Guobao>>

Thanks Matthias! It will be great for passing the model to the paramservfunction.

Regards,Guobao

2018-05-10 21:47 GMT+02:00 Matthias Boehm <[EMAIL PROTECTED]>:

> just FYI: we now have support for list and named-list data types in> SystemML, which allow passing the entire model as a single handle. For> example, you can define the following>> l1 = list(W1, b1, W2, b2, W3, b3, W4, b4), or> l2 = list(a=W1, b=b1, c=W2, d=b2, e=W3, f=b3, g=W4, h=b4)>> and access entries via l1[7] or l2['g'] accordingly. We're still> working on additional features to make the integration with IPA,> functions, and size/type propagation smoother, but the basic> functionality is already available.>> Regards,> Matthias>> On Sun, May 6, 2018 at 1:08 PM, Matthias Boehm <[EMAIL PROTECTED]> wrote:> > Hi Guobao,> >> > that sounds very good. In general, the "model" refers to the> > collection of all weights and bias matrices of a given architecture.> > Similar to a classic regression model, we can view the weights as the> > "slope", i.e., multiplicative terms, while the biases are the> > "intercept", i.e., additive terms that shift the layer output. Both> > are subject to training and thus part of the model.> >> > This implies that the number of matrices in the model depends on the> > architecture. Hence, we have two choices here: (a) allow for a> > variable number of inputs and outputs, or (b) create a struct-like> > data type that allows passing the collection of matrices via a single> > handle. We've discussed the second option in other contexts as well> > because this would also be useful for reducing the number of> > parameters passed through function calls. I'm happy to help out> > integrating these struct-like data types if needed.> >> > Great to see that you're in the process of updating the related JIRAs.> > Let us know whenever you think you're ready with an initial draft -> > then I'd make a detailed pass over it.> >> > Furthermore, I would recommend to experiment with running these> > existing mnist lenet examples (which is one of our baselines moving> > forward):> > * Download the "infinite MNIST" data generator> > (http://leon.bottou.org/projects/infimnist), and generate a moderately> > sized dataset (e.g., 256K instances).> > * Convert the input into SystemML's binary block format. The generator> > produces the data in libsvm format and we provide a data converter> > (see RDDConverterUtils.libsvmToBinaryBlock) to convert this into our> > internal binary representation.> > * Run the basic mnist lenet example for a few epochs.> > * Install the native BLAS libraries mkl or openblas and try using it> > for the above example to ensure its setup and configured correctly.> >> >> > Regards,> > Matthias> >> > On Sun, May 6, 2018 at 3:24 AM, Guobao Li <[EMAIL PROTECTED]>> wrote:> >> Hi Matthias,> >>> >> I'm currently reading the dml script MNIST LeNet example and got some> >> questions. I hope that you could help me out of them.> >>> >> 1) Is it possible to define a matrix containing the variables? Because> I'm> >> wondering how to represent the model as a parameter for the "paramserv"> >> function.> >> 2) What is the role of bias? Why we need it?> >>> >> Additionally, I have added some updates in JIRA for SYSTEMML-2083 and> hope> >> to get some feedback. Thanks!> >>> >> Regards,> >> Guobao> >>>

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